Selection efficiencies for the barrel region at object level. Definitions used in this table: Track-Match efficiency (barrel / endcap) is the fraction of reconstructed superclusters matched to at least two tracks (within a ΔR < 0.15 cone) with $p_T$>25GeV, normalized to the number of generated electron pairs. Merged-electron-pair (MEP) reconstruction efficiency (barrel / endcap) is the fraction of events in which an MEP is successfully reconstructed, using the same normalization. Iso-cuts efficiency (barrel / endcap) is the fraction of reconstructed MEPs that pass the isolation requirement. BDT efficiency (barrel / endcap) is the fraction of MEPs that pass the BDT selection among those passing the isolation requirement. Merged-electron-pair ID efficiency (barrel / endcap) is the fraction of MEPs that satisfy all signal-selection criteria, again normalized to the number of generated electron pairs.

Selection efficiencies for the endcap region at object level. Definitions as above.

Trigger-level and overall event-level efficiency.Definitions used in this table: Trigger efficiency is defined as the number of events passing both the trigger and the HAA4e offline selection, divided by the number passing the offline selection alone. Number of normalized events passing all selections gives the luminosity-scaled event yield after applying all cuts. Total event efficiency is the fraction of generated events in which a four-electron invariant mass is successfully reconstructed within the 80-250 GeV range, with event weights applied.

Selection efficiencies for the barrel region at object level. Definitions used in this table: Track-Match efficiency (barrel / endcap) is the fraction of reconstructed superclusters matched to at least two tracks (within a ΔR < 0.15 cone) with $p_T$>25GeV, normalized to the number of generated electron pairs. Merged-electron-pair (MEP) reconstruction efficiency (barrel / endcap) is the fraction of events in which an MEP is successfully reconstructed, using the same normalization. Iso-cuts efficiency (barrel / endcap) is the fraction of reconstructed MEPs that pass the isolation requirement. BDT efficiency (barrel / endcap) is the fraction of MEPs that pass the BDT selection among those passing the isolation requirement. Merged-electron-pair ID efficiency (barrel / endcap) is the fraction of MEPs that satisfy all signal-selection criteria, again normalized to the number of generated electron pairs.

Selection efficiencies for the endcap region at object level. Definitions as above.

Trigger-level and overall event-level efficiency.Definitions used in this table: Trigger efficiency is defined as the number of events passing both the trigger and the HAA4e offline selection, divided by the number passing the offline selection alone. Number of normalized events passing all selections gives the luminosity-scaled event yield after applying all cuts. Total event efficiency is the fraction of generated events in which a four-electron invariant mass is successfully reconstructed within the 80-250 GeV range, with event weights applied.

Selection efficiencies for the barrel region at object level. Definitions used in this table: Track-Match efficiency (barrel / endcap) is the fraction of reconstructed superclusters matched to at least two tracks (within a ΔR < 0.15 cone) with $p_T$>25GeV, normalized to the number of generated electron pairs. Merged-electron-pair (MEP) reconstruction efficiency (barrel / endcap) is the fraction of events in which an MEP is successfully reconstructed, using the same normalization. Iso-cuts efficiency (barrel / endcap) is the fraction of reconstructed MEPs that pass the isolation requirement. BDT efficiency (barrel / endcap) is the fraction of MEPs that pass the BDT selection among those passing the isolation requirement. Merged-electron-pair ID efficiency (barrel / endcap) is the fraction of MEPs that satisfy all signal-selection criteria, again normalized to the number of generated electron pairs.

Selection efficiencies for the endcap region at object level. Definitions as above.

Trigger-level and overall event-level efficiency.Definitions used in this table: Trigger efficiency is defined as the number of events passing both the trigger and the HAA4e offline selection, divided by the number passing the offline selection alone. Number of normalized events passing all selections gives the luminosity-scaled event yield after applying all cuts. Total event efficiency is the fraction of generated events in which a four-electron invariant mass is successfully reconstructed within the 80-250 GeV range, with event weights applied.

Selection efficiencies for the barrel region at object level. Definitions used in this table: Track-Match efficiency (barrel / endcap) is the fraction of reconstructed superclusters matched to at least two tracks (within a ΔR < 0.15 cone) with $p_T$>25GeV, normalized to the number of generated electron pairs. Merged-electron-pair (MEP) reconstruction efficiency (barrel / endcap) is the fraction of events in which an MEP is successfully reconstructed, using the same normalization. Iso-cuts efficiency (barrel / endcap) is the fraction of reconstructed MEPs that pass the isolation requirement. BDT efficiency (barrel / endcap) is the fraction of MEPs that pass the BDT selection among those passing the isolation requirement. Merged-electron-pair ID efficiency (barrel / endcap) is the fraction of MEPs that satisfy all signal-selection criteria, again normalized to the number of generated electron pairs.

Selection efficiencies for the endcap region at object level. Definitions as above.

Trigger-level and overall event-level efficiency.Definitions used in this table: Trigger efficiency is defined as the number of events passing both the trigger and the HAA4e offline selection, divided by the number passing the offline selection alone. Number of normalized events passing all selections gives the luminosity-scaled event yield after applying all cuts. Total event efficiency is the fraction of generated events in which a four-electron invariant mass is successfully reconstructed within the 80-250 GeV range, with event weights applied.

$M_{\tau}$ distribution in signal region, (OS$\mu$, Belle II)

$M_{\tau}$ distribution in signal region, (SS$e$, Belle)

$M_{\tau}$ distribution in signal region, (SS$e$, Belle II)

$M_{\tau}$ distribution in signal region, (SS$\mu$, Belle)

$M_{\tau}$ distribution in signal region, (SS$\mu$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$ and $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (OS$e$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$ and $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (OS$e$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$ and $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (OS$\mu$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$ and $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (OS$\mu$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$ and $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (SS$e$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$ and $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (SS$e$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$ and $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (SS$\mu$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$ and $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (SS$\mu$, Belle II)

Efficiency as function the helicity angle of the kaon $\cos\theta_{K}$, and the helicity angle of the lepton $\cos\theta_{\ell}$ in bin e the four-momentum-transfer $q^{2}$, (OS$e$, Belle)

Efficiency as function the helicity angle of the kaon $\cos\theta_{K}$, and the helicity angle of the lepton $\cos\theta_{\ell}$ in bin e the four-momentum-transfer $q^{2}$, (OS$e$, Belle II)

Efficiency as function the helicity angle of the kaon $\cos\theta_{K}$, and the helicity angle of the lepton $\cos\theta_{\ell}$ in bin e the four-momentum-transfer $q^{2}$, (OS$\mu$, Belle)

Efficiency as function the helicity angle of the kaon $\cos\theta_{K}$, and the helicity angle of the lepton $\cos\theta_{\ell}$ in bin e the four-momentum-transfer $q^{2}$, (OS$\mu$, Belle II)

Efficiency as function the helicity angle of the kaon $\cos\theta_{K}$, and the helicity angle of the lepton $\cos\theta_{\ell}$ in bin e the four-momentum-transfer $q^{2}$, (SS$e$, Belle)

Efficiency as function the helicity angle of the kaon $\cos\theta_{K}$, and the helicity angle of the lepton $\cos\theta_{\ell}$ in bin e the four-momentum-transfer $q^{2}$, (SS$e$, Belle II)

Efficiency as function the helicity angle of the kaon $\cos\theta_{K}$, and the helicity angle of the lepton $\cos\theta_{\ell}$ in bin e the four-momentum-transfer $q^{2}$, (SS$\mu$, Belle)

Efficiency as function the helicity angle of the kaon $\cos\theta_{K}$, and the helicity angle of the lepton $\cos\theta_{\ell}$ in bin e the four-momentum-transfer $q^{2}$, (SS$\mu$, Belle II)

Efficiency as function of the helicity angle of the kaon $\cos\theta_{K}$, (OS$e$, Belle)

Efficiency as function of the helicity angle of the kaon $\cos\theta_{K}$, (OS$e$, Belle II)

Efficiency as function of the helicity angle of the kaon $\cos\theta_{K}$, (OS$\mu$, Belle)

Efficiency as function of the helicity angle of the kaon $\cos\theta_{K}$, (OS$\mu$, Belle II)

Efficiency as function of the helicity angle of the kaon $\cos\theta_{K}$, (SS$e$, Belle)

Efficiency as function of the helicity angle of the kaon $\cos\theta_{K}$, (SS$e$, Belle II)

Efficiency as function of the helicity angle of the kaon $\cos\theta_{K}$, (SS$\mu$, Belle)

Efficiency as function of the helicity angle of the kaon $\cos\theta_{K}$, (SS$\mu$, Belle II)

Efficiency as function of the helicity angle of the lepton $\cos\theta_{\ell}$, (OS$e$, Belle)

Efficiency as function of the helicity angle of the lepton $\cos\theta_{\ell}$, (OS$e$, Belle II)

Efficiency as function of the helicity angle of the lepton $\cos\theta_{\ell}$, (OS$\mu$, Belle)

Efficiency as function of the helicity angle of the lepton $\cos\theta_{\ell}$, (OS$\mu$, Belle II)

Efficiency as function of the helicity angle of the lepton $\cos\theta_{\ell}$, (SS$e$, Belle)

Efficiency as function of the helicity angle of the lepton $\cos\theta_{\ell}$, (SS$e$, Belle II)

Efficiency as function of the helicity angle of the lepton $\cos\theta_{\ell}$, (SS$\mu$, Belle)

Efficiency as function of the helicity angle of the lepton $\cos\theta_{\ell}$, (SS$\mu$, Belle II)

Efficiency as function of the acoplanarity angle $\phi$, (OS$e$, Belle)

Efficiency as function of the acoplanarity angle $\phi$, (OS$e$, Belle II)

Efficiency as function of the acoplanarity angle $\phi$, (OS$\mu$, Belle)

Efficiency as function of the acoplanarity angle $\phi$, (OS$\mu$, Belle II)

Efficiency as function of the acoplanarity angle $\phi$, (SS$e$, Belle)

Efficiency as function of the acoplanarity angle $\phi$, (SS$e$, Belle II)

Efficiency as function of the acoplanarity angle $\phi$, (SS$\mu$, Belle)

Efficiency as function of the acoplanarity angle $\phi$, (SS$\mu$, Belle II)

Efficiency as function of the four-momentum-transfer $q^{2}$, (OS$e$, Belle)

Efficiency as function of the four-momentum-transfer $q^{2}$, (OS$e$, Belle II)

Efficiency as function of the four-momentum-transfer $q^{2}$, (OS$\mu$, Belle)

Efficiency as function of the four-momentum-transfer $q^{2}$, (OS$\mu$, Belle II)

Efficiency as function of the four-momentum-transfer $q^{2}$, (SS$e$, Belle)

Efficiency as function of the four-momentum-transfer $q^{2}$, (SS$e$, Belle II)

Efficiency as function of the four-momentum-transfer $q^{2}$, (SS$\mu$, Belle)

Efficiency as function of the four-momentum-transfer $q^{2}$, (SS$\mu$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$, (OS$e$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$, (OS$e$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$, (OS$\mu$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$, (OS$\mu$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$, (SS$e$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$, (SS$e$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$, (SS$\mu$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}\ell)$, (SS$\mu$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (OS$e$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (OS$e$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (OS$\mu$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (OS$\mu$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (SS$e$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (SS$e$, Belle II)

Efficiency as function of the invariant mass squared $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (SS$\mu$, Belle)

Efficiency as function of the invariant mass squared $m^2(K^{*0}t_{\tau})$, where $t_{\tau}$ is the track from the $\tau$, (SS$\mu$, Belle II)

Distributions of the number of hits in the cluster (Nhits) for the DT category in the signal region (SR). The last histogram bin contains all overflow events.

Distributions of the number of hits in the cluster (Nhits) for the DT category in the out-of-time (OOT) region. The last histogram bin contains all overflow events.

Distributions of the number of hits in the cluster (Nhits) for the CSC category in the signal region (SR). The last histogram bin contains all overflow events.

Distributions of the number of hits in the cluster (Nhits) for the CSC category in the out-of-time (OOT) region. The last histogram bin contains all overflow events.

Distributions of the number of hits in the cluster (Nhits) for the DT category in the out-of-time (OOT) region. The last histogram bin contains all overflow events.

The 95% CL observed and expected upper limits on the VLL production cross section as a function of the VLL mass for the pseudoscalar mean proper decay length c$\tau$ = 0.025 m. The pseudoscalar mass is 2 GeV.

Distributions of the number of hits in the cluster (Nhits) for the CSC category in the out-of-time (OOT) region. The last histogram bin contains all overflow events.

The 95% CL observed and expected upper limits on the VLL production cross section as a function of the pseudoscalar mean proper decay length c$\tau$ for VLL Mass of 700 GeV. The pseudoscalar mass is 2 GeV.

The 95% CL observed and expected upper limits on the VLL production cross section as a function of the VLL mass for the pseudoscalar mean proper decay length c$\tau$ = 0.025 m. The pseudoscalar mass is 2 GeV.

The 95% CL observed upper limits on the VLL production cross section as a function of the VLL mass and the pseudoscalar mean proper decay length c$\tau$. The pseudoscalar mass is 2 GeV.

The 95% CL observed and expected upper limits on the VLL production cross section as a function of the pseudoscalar mean proper decay length c$\tau$ for VLL Mass of 700 GeV. The pseudoscalar mass is 2 GeV.

The 95% CL observed exclusion contour on the VLL production cross section as a function of the VLL mass and the pseudoscalar mean proper decay length c$\tau$. The pseudoscalar mass is 2 GeV.

The 95% CL observed upper limits on the VLL production cross section as a function of the VLL mass and the pseudoscalar mean proper decay length c$\tau$. The pseudoscalar mass is 2 GeV.

Selection efficiencies in the CSC category signal region (SR) for different signal hypothesis. The pseudoscalar mass is 2 GeV.

The 95% CL observed exclusion contour on the VLL production cross section as a function of the VLL mass and the pseudoscalar mean proper decay length c$\tau$. The pseudoscalar mass is 2 GeV.

Selection efficiencies in the DT category signal region (SR) for different signal hypothesis. The pseudoscalar mass is 2 GeV.

Selection efficiencies in the CSC category signal region (SR) for different signal hypothesis. The pseudoscalar mass is 2 GeV.

Selection efficiencies in the DT category signal region (SR) for different signal hypothesis. The pseudoscalar mass is 2 GeV.

The cluster reconstruction efficiency, including both DT and CSC clusters, as a function of the simulated r and |z| decay positions of the pseudoscalar into photons in events with MET > 200 GeV, for a VLL mass of 300 GeV and a pseudoscalar mass of 2 GeV, and a range of ctau values uniformly distributed between 0.01 and 0.1 m.

The cluster reconstruction efficiency, including both DT and CSC clusters, as a function of the simulated r and |z| decay positions of the pseudoscalar into photons in events with MET > 200 GeV, for a VLL mass of 500 GeV and a pseudoscalar mass of 2 GeV, and a range of ctau values uniformly distributed between 0.01 and 0.1 m.

Level-1 muon trigger efficiency in cosmic-ray muon data (blue) and signal simulation (red) as a function of $d_0$, for the Level-1 trigger $p_T$ threshold used in the 2018 analysis triggers. The denominator in the efficiency calculation is the number of STA muons with $|\eta| < 1.2$ and $p_T > 28$ GeV.

Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA-to-TMS muon association procedure, as a function of true $L_{xy}$, in all simulated $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal samples combined. The fractions are computed relative to the number of signal events passing the trigger and containing two STA muons with more than 12 muon detector hits and $p_T > 10$ GeV matched to generated muons from $X \rightarrow \mu \mu$ decays.

Fractions of signal events with zero (green), one (blue), and two (red) STA muons matched to TMS muons by the STA-to-TMS muon association procedure, as a function of true $L_{xy}$, in all simulated $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal samples combined. The fractions are computed relative to the number of signal events passing the trigger and containing two STA muons with more than 12 muon detector hits and $p_T > 10$ GeV matched to generated muons from $X \rightarrow \mu \mu$ decays.

Comparison of the number of events observed in 2016 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2016 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2018 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2018 data in the STA-STA dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2016 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2016 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2018 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2018 data in the STA-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 30 and 60 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legends also include the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{\mu \mu}$ intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $6 < min(d_0 / \sigma_{d_0}) \leq 10$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $10 < min(d_0 / \sigma_{d_0}) \leq 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, in representative $m_{\mu \mu}$ intervals in the $min(d_0 / \sigma_{d_0}) > 20$ bin. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility. The legend also includes the total number of observed events as well as the number of expected background events obtained inclusively, by applying the background evaluation method to the events in all $m_{Z_D}$ and min($d_0 / \sigma_{d_0}$) intervals combined.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.

Comparison of the number of events observed in 2016 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.

Comparison of the number of events observed in 2018 data in the TMS-TMS dimuon category with the expected number of background events, as a function of the smaller of the two $d_0 / \sigma_{d_0}$ values in the TMS-TMS dimuon. The black points with crosses show the number of observed events; the green and yellow components of the stacked histograms represent the estimated numbers of DY and QCD events, respectively. The last bin includes events in the overflow. The uncertainties in the total expected background (shaded area) are statistical only. Signal contributions expected from simulated $H \rightarrow Z_D Z_D$ with $m_{Z_D}$ of 20 and 50 GeV are shown in red and blue, respectively. Their yields are set to the corresponding combined median expected exclusion limits at 95% CL, scaled up as indicated in the legend to improve visibility.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(\Phi \rightarrow XX)B(X \rightarrow \mu \mu)$ as a function of $c\tau(X)$ in the heavy-scalar model, for $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 10\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 20\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 30\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 40\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 50\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

The 95% CL upper limits on $\sigma(H \rightarrow Z_DZ_D)B(Z_D \rightarrow \mu \mu)$ as a function of $c\tau(Z_D)$ in the HAHM model, for $m_{Z_D} = 60\ GeV$. The median expected limits obtained from the STA-STA, STA-TMS, and TMS-TMS dimuon categories are shown as dashed green, blue, and red curves, respectively; the combined median expected limits are shown as dashed black curves; the combined observed limits are shown as solid black curves. The green and yellow bands correspond, respectively, to the 68 and 95% quantiles for the combined expected limits. The horizontal lines in gray correspond to the theoretical predictions for values of $B(H \rightarrow Z_DZ_D)$ indicated next to the lines.

Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $c\tau(Z_D)$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.

Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $c\tau(Z_D)$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.

Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $\epsilon$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.

Observed 95% CL exclusion contours in the HAHM model, in the ($m(Z_D)$, $\epsilon$) plane. The contours correspond to several representative values of $B(H \rightarrow Z_DZ_D$) ranging from 0.005 to 1%.

Background estimation and observed number of events in the STA-STA dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown. The mass interval is followed by the estimated and observed counts for the given year. The quoted uncertainties are statistical only.

Background estimations and observed numbers of events in the STA-STA dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown, followed by the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ for the given year. The quoted uncertainties are statistical only.

Background estimation and observed number of events in the TMS-TMS dimuon category in 2016 data. The mass interval is followed by the estimated and observed counts within each $min(d_0 / \sigma_{d_0})$ bin in this mass interval. The quoted uncertainties are statistical only.

Background estimations and observed numbers of events in the TMS-TMS dimuon category in 2016 data. For each mass interval, the table shows the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ in each of the three $\text{min}(d_0 / \sigma_{d_0})$ bins. The quoted uncertainties are statistical only

Background estimation and observed number of events in the TMS-TMS dimuon category in 2018 data. The mass interval is followed by the estimated and observed counts within each $min(d_0 / \sigma_{d_0})$ bin in this mass interval. The quoted uncertainties are statistical only.

Background estimations and observed numbers of events in the TMS-TMS dimuon category in 2016 data. For each mass interval, the table shows the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ in each of the three $\text{min}(d_0 / \sigma_{d_0})$ bins. The quoted uncertainties are statistical only

Correspondence between the mass intervals in the TMS-TMS category and the parameters of the simulated signal samples.

Correspondence between the probed LLP masses and the chosen mass intervals in the TMS-TMS category.

Background estimation and observed number of events in the STA-TMS dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown. The mass interval is followed by the estimated and observed counts for the given year. The quoted uncertainties are statistical only.

Background estimations and observed numbers of events in the STA-TMS dimuon category in 2016 and 2018 data. For each probed LLP mass, the chosen mass interval is shown, followed by the predicted background yield $N^\text{est}_\text{bkg}$ and the observed number of events $N^\text{obs}$ for the given year. The quoted uncertainties are statistical only.

Number of events passing consecutive sets of selection criteria for 2018 collision data and the signal process $\Phi(125) \rightarrow XX(20\ GeV, c\tau = 13\ cm) \rightarrow \mu\mu$. Each row introduces a new criterion that is applied in addition to the selection of the previous row. In addition to the total number of events, N(events), the event yields of the individual dimuon vertex categories, STA-STA, TMS-TMS, and STA-TMS, are shown in separate columns for each data set. In these columns, events containing selected dimuons of different categories are independently counted for each category.

Number of events passing consecutive sets of selection criteria, in 2018 data and in a sample of simulated $\Phi \rightarrow XX \rightarrow \mu\mu$ signal events with $m(H) = 125\ GeV$, $m(X) = 20\ GeV$, and $c\tau = 13\ cm$. Each row introduces a new criterion that is applied in addition to the selection of the previous row. In addition to the total number of events $N(\text{total})$, the event yields in the individual dimuon categories, STA-STA, TMS-TMS, and STA-TMS, are shown in separate columns for each data set. In these columns, events containing selected dimuons of different categories are counted independently for each category.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 350\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 125\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 200\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 400\ GeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 150\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 150\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1000\ GeV$ and $m(X) = 350\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $\Phi \rightarrow XX \rightarrow 4\mu$ signal process with $m(\Phi) = 1\ TeV$ and $m(X) = 350\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 10\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 10\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 20\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 20\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 30\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 30\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 40\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 40\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 50\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 50\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 60\ GeV$. The figure shows efficiencies in the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well as the combined efficiency (black) calculated as the sum of the efficiencies of the individual categories. The signal efficiencies for the 2016 and 2018 datasets are shown as dashed and solid lines, respectively.

Overall signal efficiencies as a function of $c\tau$ for the $H \rightarrow Z_DZ_D \rightarrow \mu\mu + anything$ signal process with $m(H) = 125\ GeV$ and $m(Z_D) = 60\ GeV$. The plot shows efficiencies of the three dimuon categories, STA-STA (green), TMS-TMS (red), and STA-TMS (blue), as well the combined efficiency (black). Each efficiency is computed as the ratio of the number of simulated signal events in which at least one dimuon candidate of a given type (or any type for the combined efficiency) passes all selection criteria (including the trigger) to the total number of simulated signal events. All efficiencies are corrected by the data-to-simulation scale factors described in the paper. The efficiencies in the 2016 and 2018 data sets are shown as dashed and solid curves, respectively.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $L_{xy}^\mathrm{true} < 20\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $20\ cm < L_{xy}^\mathrm{true} < 70\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the TMS-TMS dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2016 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper. Efficiencies for dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the STA-TMS and TMS-TMS dimuon categories are equal to zero.

Signal efficiencies as a function of the smaller of the two values of generated muon $p_T$ and $d_0$ in dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the $\Phi \rightarrow XX \rightarrow \mu\mu + anything$ signal model, in 2018 samples. The efficiency in each bin is computed as the ratio of the number of simulated signal dimuons in that bin that pass the trigger requirements and selection criteria applied in the STA-STA dimuon category to the total number of simulated signal dimuons in that bin and within the geometric acceptance. The geometric acceptance is defined as the generated longitudinal decay length $L_{z}$ smaller than $8\ m$ and $|\eta^\mathrm{true}|$ of both generated muons forming the dimuon smaller than 2.0. The efficiencies obtained from simulation were further corrected by the data-to-simulation scale factors described in the paper. Efficiencies for dimuons with $70\ cm < L_{xy}^\mathrm{true} < 320\ cm$ in the STA-TMS and TMS-TMS dimuon categories are equal to zero.

Distributions of MaxMin alternative algorithm $m(h_{\mathrm{cand1}},h_{\mathrm{cand2}})_{\mathrm{avg}}$ after the background-only fit. The backgrounds which contribute only a small amount (diboson, W+jets and ttbar+W/Z/h) are grouped and labelled as `Other'.

Distributions of Leading jet $p_T$ after the background-only fit. The backgrounds which contribute only a small amount (diboson, W+jets and ttbar+W/Z/h) are grouped and labelled as `Other'.

Distributions of MaxMin algorithm $m_{hcand}$ after the background-only fit. The backgrounds which contribute only a small amount (diboson, W+jets and ttbar+W/Z/h) are grouped and labelled as `Other'.

Signal efficiency in SRA_M_m60 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRC_28 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRC_26 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRC_24 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_M_dm130 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRB for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_L_dm130 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRC_incl for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_L_m60 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_incl_dm130 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal acceptance in SRA_incl_m60 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRA_H_m60 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRA_L_dm130 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRB for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRC_22 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRA_H_dm130 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRC_24 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRC_26 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRA_H_m60 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRA_incl_m60 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRC_22 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRA_M_m60 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRC_28 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal acceptance in SRA_H_dm130 for simplified models with $\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$ production

Signal efficiency in SRA_incl_dm130 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRA_L_m60 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRA_M_dm130 for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Signal efficiency in SRC_incl for simplified models with '$\widetilde{b}\widetilde{b}$ $\rightarrow$ $b\bar{b} \widetilde{\chi}_2^0 \widetilde{\chi}_2^0$ $\rightarrow$ hh$\widetilde{\chi}_1^0 \widetilde{\chi}_1^0$, h $\rightarrow$ $b\bar{b}$' production

Observed 95% CLs exclusion limit for the $\Delta M(\widetilde{\chi}_{2}^{0},\widetilde{\chi}_{1}^{0})$=130GeV signal grid for the best combined signal regions.

Expected 95% CLs exclusion limit for the $\Delta M(\widetilde{\chi}_{2}^{0},\widetilde{\chi}_{1}^{0})$=130GeV signal grid for the best combined signal regions.

Observed 95% CLs exclusion limit for the $M(\widetilde{\chi}_{1}^{0})$=60GeV signal grid for the best combined signal regions.

Expected 95% CLs exclusion limit for the $M(\widetilde{\chi}_{1}^{0})$=60GeV signal grid for the best combined signal regions.

Model dependent upper limit on the best combined signal regions considered in the $\Delta M(\widetilde{\chi}_{2}^{0},\widetilde{\chi}_{1}^{0})$=130GeV signal grid

Model dependet upper limits on the best combined signal regions considered in the $\Delta M(\widetilde{\chi}_{2}^{0},\widetilde{\chi}_{1}^{0})$=130GeV signal grid

Result of background only fit applied to signal regions. Event yields from the signal regions compared with SM MC predictions for the 3 highest contributing backgrounds separately and combined minor backgrounds.

Expected background event yields and dominant systematic uncertainties on background estimates in the A-type (inclusive), B-type and C-type (inclusive) regions.

Background-only fit results for the A- and B-type regions performed using 139$fb^{-1}$ of data. The quoted uncertainties on the fitted SM background include both the statistical and systematic uncertainties.

Background-only fit results for the C-type region performed using 139$fb^{-1}$ of data. The quoted uncertainties on the fitted SM background include both the statistical and systematic uncertainties.

Observed 95% CL upper limits on the visible cross sections σvis, the observed (S95obs) and expected (S95exp) 95% CL upper limits on the number of signal events with ± 1 σ excursions of the expectation, the CL of the background-only hypothesis, CLB, the discovery p-value (p0), truncated at 0.5, and the associated significance.

Cutflow of the MC events scaled to 139 $fb^{-1}$ for the SRA selections, with a scalar bottom signal of m$(\widetilde{b}_{1},\widetilde{\chi}_2^0,\widetilde{\chi}_1^0) = (1100, 330, 200)$ GeV, considered.

Cutflow of the MC events scaled to 139 $fb^{-1}$ for the SRB selections, with a scalar bottom signal of m$(\widetilde{b}_{1},\widetilde{\chi}_2^0,\widetilde{\chi}_1^0) = (700, 680, 550)$ GeV, considered.

Cutflow of the MC events scaled to 139 $fb^{-1}$ for the SRC selections, with a scalar bottom signal of m$(\widetilde{b}_{1},\widetilde{\chi}_2^0,\widetilde{\chi}_1^0) = (1200, 1150, 60)$ GeV, considered.

Cutflow table for a pair produced coloron of 1500 GeV decaying into two quarks.

The observed number of data, background and top squark signal events in each of the signal regions of the inclusive selection

The observed number of data, background and coloron signal events in each of the signal regions of the inclusive selection

The observed number of data, background and top squark signal events in each of the signal regions of the b-tagged selection

Signal acceptance and efficiency (in %) as a function of M(STOP), before mass windows

Signal acceptance (in %) and efficiency as a function of M(STOP), after mass windows

Signal acceptance and efficiency (in %) as a function of M(RHO), before mass windows

Signal acceptance and efficiency (in %) as a function of M(RHO), after mass windows

Signal acceptance (in %) and efficiency as a function of M(STOP), before mass windows

Signal acceptance (in %) and efficiency as a function of M(STOP), after mass windows

Cross section excluded at 95% CL as a function of the top squark mass, for a pair produced top squark with decays into a pair of light-quarks.

Cross section excluded at 95% CL as a function of the cooron mass, for a pair produced coloron with decays into a pair of light-quarks.

Cross section excluded at 95% CL as a function of the top squark mass, for a pair produced top squark with decays into a b- and an s-quark.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 700 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 800 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 800 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 900 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 900 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1000 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1000 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1050 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1050 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1100 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1100 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1150 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1150 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1200 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1200 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1250 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1250 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1300 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1300 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1350 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1350 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1400 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1400 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1450 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1450 GeV stop. All limits are computed at 95% CL.

Expected exclusion limit contour in the (BRe,BRtau) plane for a 1500 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit contour in the (BRe,BRtau) plane for a 1500 GeV stop. All limits are computed at 95% CL.

Signal acceptance (in %) in the (BRe,BRtau) plane for a 800 GeV stop, for the SR800 signal region.

Signal acceptance (in %) in the (BRe,BRtau) plane for a 800 GeV stop, for the SR1100 signal region.

Signal efficiency (in %) in the (BRe,BRtau) plane for a 800 GeV stop, for the SR800 signal region.

Signal efficiency (in %) in the (BRe,BRtau) plane for a 800 GeV stop, for the SR1100 signal region.

Signal acceptance (in %) in the (BRe,BRtau) plane for a 1200 GeV stop, for the SR800 signal region.

Signal acceptance (in %) in the (BRe,BRtau) plane for a 1200 GeV stop, for the SR1100 signal region.

Signal efficiency (in %) in the (BRe,BRtau) plane for a 1200 GeV stop, for the SR800 signal region.

Signal efficiency (in %) in the (BRe,BRtau) plane for a 1200 GeV stop, for the SR1100 signal region.

Signal acceptance (in %) in the (BRe,BRtau) plane for a 1500 GeV stop, for the SR800 signal region.

Signal acceptance (in %) in the (BRe,BRtau) plane for a 1500 GeV stop, for the SR1100 signal region.

Signal efficiency (in %) in the (BRe,BRtau) plane for a 1500 GeV stop, for the SR800 signal region.

Signal efficiency (in %) in the (BRe,BRtau) plane for a 1500 GeV stop, for the SR1100 signal region.

$m_{bl}^{0}$ distribution in SR800. All selection criteria are applied, except the selection on the variable that is displayed in the plot. The SM backgrounds are normalized to the values determined in the background-only fit. The last bin includes overflows.

$m_{bl}^\mathrm{asym}$ distribution in SR800. All selection criteria are applied, except the selection on the variable that is displayed in the plot. The SM backgrounds are normalized to the values determined in the background-only fit. The last bin includes overflows.

$H_\mathrm{T}$ distribution in SR800. All selection criteria are applied, except the selection on the variable that is displayed in the plot. The SM backgrounds are normalized to the values determined in the background-only fit. The last bin includes overflows.

$m_{ll}$ distribution in SR800. All selection criteria are applied, except the selection on the variable that is displayed in the plot. The SM backgrounds are normalized to the values determined in the background-only fit. The last bin includes overflows.

$m_{bl}^{1}$(rej) distribution in SR800. All selection criteria are applied, except the selection on the variable that is displayed in the plot. The SM backgrounds are normalized to the values determined in the background-only fit. The last bin includes overflows.

Full list of event selections and MC generator-weighted yields and efficiencies in the inclusive SR800 and SR1100 signal regions for several signal samples of varying stop mass with decay into b-electron, b-muon or b-tau at 1/3 branching ratio.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 600 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 700 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 800 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 900 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1000 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1050 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1100 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1150 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1200 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1250 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1300 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1350 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1400 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1450 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1500 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1550 GeV stop. All limits are computed at 95% CL.

Observed exclusion limit in the (BRe,BRtau) plane on the cross section for a 1600 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1350 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1400 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1450 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1500 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1550 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1600 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 600 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 700 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 800 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 900 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1000 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1050 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1100 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1150 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1200 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1250 GeV stop. All limits are computed at 95% CL.

The chosen signal region in the (BRe,BRtau) plane with the best expected exclusion on the cross section for a 1300 GeV stop. All limits are computed at 95% CL.

Dijet mass distributions for data in the (a) WW signal region.

Dijet mass distributions for data in the (b) WZ signal region.

Dijet mass distributions for data in the (c) ZZ signal region.

Dijet mass distributions for data in the (d) WZ+WW signal region.

Dijet mass distributions for data in the (e) WW+ZZ signal region.

Upper limits at the 95% CL on the cross section times branching ratio for WW+WZ production as a function of V' mass

Upper limits at the 95% CL on the cross section times branching ratio for WW+ZZ production as a function of GKK mass for the bulk RS model with k/M̄Pl=1.

Upper limits at the 95% CL on the cross section times branching ratio for (c) WW+ZZ production as a function of scalar mass.

Upper limits at the 95% CL on the cross section times branching ratio for WW production as a function of V' mass

Upper limits at the 95% CL on the cross section times branching ratio for WZ production as a function of V' mass

Upper limits at the 95% CL on the cross section times branching ratio for WW production as a function of GKK mass for the bulk RS model with k/M̄Pl=0.5.

Upper limits at the 95% CL on the cross section times branching ratio for ZZ production as a function of GKK mass for the bulk RS model with k/M̄Pl=0.5.

Upper limits at the 95% CL on the cross section times branching ratio for WW production as a function of GKK mass for the bulk RS model with k/M̄Pl=1.

Upper limits at the 95% CL on the cross section times branching ratio for ZZ production as a function of GKK mass for the bulk RS model with k/M̄Pl=1.

Upper limits at the 95% CL on the cross section times branching ratio for WW+ZZ production as a function of GKK mass for the bulk RS model with k/M̄Pl=0.5.

Upper limits at the 95% CL on the cross section times branching ratio for WW production as a function of scalar mass.

Upper limits at the 95% CL on the cross section times branching ratio for ZZ production as a function of scalar mass.

Observed 95% CL limit for the pMSSM grid when the signal cross section is decreased by one standard deviation.

Expected 95% CL limit for the pMSSM grid.

Expected 95% CL limit for the pMSSM grid with an up variation of the uncertainties.

Expected 95% CL limit for the pMSSM grid with a down variation of the uncertainties.

Observed 95% CL limit for the 2Step grid.

Observed 95% CL limit for the 2Step grid when the signal cross section is increased by one standard deviation.

Observed 95% CL limit for the 2Step grid when the signal cross section is decreased by one standard deviation.

Expected 95% CL limit for the 2Step grid.

Expected 95% CL limit for the 2Step grid with an up variation of the uncertainties.

Expected 95% CL limit for the 2Step grid with a down variation of the uncertainties.

Observed 95% CL limit for the gtt off-shell grid.

Observed 95% CL limit for the gtt off-shell grid when the signal cross section is increased by one standard deviation.

Observed 95% CL limit for the gtt off-shell grid when the signal cross section is decreased by one standard deviation.

Expected 95% CL limit for the gtt off-shell grid.

Expected 95% CL limit for the gtt off-shell grid with an up variation of the uncertainties.

Expected 95% CL limit for the gtt off-shell grid with a down variation of the uncertainties.

Observed 95% CL limit for the RPV grid.

Observed 95% CL limit for the RPV grid when the signal cross section is increased by one standard deviation.

Observed 95% CL limit for the RPV grid when the signal cross section is decreased by one standard deviation.

Expected 95% CL limit for the RPV grid.

Expected 95% CL limit for the RPV grid with an up variation of the uncertainties.

Expected 95% CL limit for the RPV grid with a down variation of the uncertainties.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-7j80-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-7j80-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-7j80-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j80-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j80-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j80-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j80-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j80-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j80-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-8j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-9j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-10j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-11j50-0b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-11j50-1b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

Number of signal events expected for 36.1 fb$^{-1}$ at different stages of the event selection for the signal region SR-11j50-2b in a pMSSM inspired model where m($\tilde{g}$) = 1400 GeV and m($\tilde{\chi}_{0}^{1}$) = 200 GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-11j50-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-11j50-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-11j50-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-7j80-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-7j80-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-7j80-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j80-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j80-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j80-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j80-0b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j80-1b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j80-2b. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-0b-MJ340. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-8j50-0b-MJ500. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-0b-MJ340. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-9j50-0b-MJ500. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-0b-MJ340. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

$E_{\mathrm{T}}^{\mathrm{miss}} / \sqrt{H_{\mathrm{T}}}$ distribution in signal region SR-10j50-0b-MJ500. Two benchmark signal models are overlaid on the plot for comparison. Labelled `pMSSM' and `2-step', they show signal distributions from the example SUSY models (as described in the paper): a pMSSM slice model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{\pm}}$) = (1300, 200) GeV and a cascade decay model with ($m \tilde{g}$, $m \tilde{\chi_{1}^{0}}$) = (1300, 200) GeV.

Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with no b-jet requirement and a minimum transverse momentum of 50 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with one inclusive b-jet required and a minimum transverse momentum of 50 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with two inclusive b-jets required and a minimum transverse momentum of 50 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with no b-jet requirement and a minimum transverse momentum of 80 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with one inclusive b-jet required and a minimum transverse momentum of 80 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the flavour stream with two inclusive b-jets required and a minimum transverse momentum of 80 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the fat-jet stream with MJSigma above 340 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

Degree of multijet closure for signal and vaidation regions (prior to the leptonic background fit) for the fat-jet stream with MJSigma above 500 GeV. The solid lines are the pre-fit predicted numbers of events and the points are the observed numbers. The blue hatched band shows only the statistical (MC and data) uncertainty on the background estimate. The template closure uncertainty for each SR bin is given by the maximal deviation of data from prediction in any non-SR bin to its left on this plot (although those for 80 GeV regions are independent of deviations in 50 GeV regions).

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the 2Step grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the 2Step grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the 2Step grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the pMSSM grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the pMSSM grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the pMSSM grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the RPV grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the RPV grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the RPV grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the gtt off-shell grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the gtt off-shell grid.

The best-expected signal region and the corresponding best-observed and best-expected CLs values for the gtt off-shell grid.

95% CLs observed upper limit on model cross-section (in fb) for 2Step signal points for the best-expected signal region.

95% CLs observed upper limit on model cross-section (in fb) for RPV signal points for the best-expected signal region.

95% CLs observed upper limit on model cross-section (in fb) for gtt off-shell signal points for the best-expected signal region.

Performance of the SR-8j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-8j50-0b-MJ340 for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-8j50-0b-MJ500 for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-8j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-8j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-9j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-9j50-0b-MJ340 for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-9j50-0b-MJ500 for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-9j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-9j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-10j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-10j50-0b-MJ340 for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-10j50-0b-MJ500 for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-10j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-10j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-11j50-0b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-11j50-1b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-11j50-2b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-7j80-0b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-7j80-1b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-7j80-2b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-8j80-0b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-8j80-1b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-8j80-2b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-9j80-0b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-9j80-1b for the 2Step grid: fractional acceptance; fractional efficiency.

Performance of the SR-9j80-2b for the 2Step grid: fractional acceptance; fractional efficiency.